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MicroRNAs Involved in the Mitogen-Activated Protein Kinase Cascades Pathway During Glucose-Induced Cardiomyocyte Hypertrophy

  • E. Shen
    Affiliations
    Department of Ultrasound in Medicine, Shanghai Jiaotong University Affiliated 6th People's Hospital, Shanghai, China

    Cardiovascular Disease Laboratory, Shanghai Institute of Ultrasound in Medicine, Shanghai, China
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  • Xuehong Diao
    Affiliations
    Department of Ultrasound in Medicine, Shanghai Jiaotong University Affiliated 6th People's Hospital, Shanghai, China
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  • Xiaoxia Wang
    Affiliations
    Department of Nephrology, Shanghai Jiaotong University Affiliated 6th People's Hospital, Shanghai, China
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  • Ruizhen Chen
    Affiliations
    Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Bing Hu
    Correspondence
    Address reprint requests to Bing Hu, M.D., Department of Ultrasound in Medicine, Shanghai Jiaotong University Affiliated 6th People's Hospital, Shanghai Institute of Ultrasound in Medicine, NO.600, Yi Shan Road, China
    Affiliations
    Department of Ultrasound in Medicine, Shanghai Jiaotong University Affiliated 6th People's Hospital, Shanghai, China

    Cardiovascular Disease Laboratory, Shanghai Institute of Ultrasound in Medicine, Shanghai, China
    Search for articles by this author
Open ArchivePublished:June 24, 2011DOI:https://doi.org/10.1016/j.ajpath.2011.04.034
      Cardiac hypertrophy is a key structural feature of diabetic cardiomyopathy in the late stage of diabetes. Recent studies show that microRNAs (miRNAs) are involved in the pathogenesis of cardiac hypertrophy in diabetic mice, but more novel miRNAs remain to be investigated. In this study, diabetic cardiomyopathy, characterized by hypertrophy, was induced in mice by streptozotocin injection. Using microarray analysis of myocardial tissue, we were able to identify changes in expression in 19 miRNA, of which 16 miRNAs were further validated by real-time PCR and a total of 3212 targets mRNA were predicted. Further analysis showed that 31 GO functions and 16 KEGG pathways were enriched in the diabetic heart. Of these, MAPK signaling pathway was prominent. In vivo and in vitro studies have confirmed that three major subgroups of MAPK including ERK1/2, JNK, and p38, are specifically upregulated in cardiomyocyte hypertrophy during hyperglycemia. To further explore the potential involvement of miRNAs in the regulation of glucose-induced cardiomyocyte hypertrophy, neonatal rat cardiomyocytes were exposed to high glucose and transfected with miR-373 mimic. Overexpression of miR-373 decreased the cell size, and also reduced the level of its target gene MEF2C, and miR-373 expression was regulated by p38. Our data highlight an important role of miRNAs in diabetic cardiomyopathy, and implicate the reliability of bioinformatics analysis in shedding light on the mechanisms underlying diabetic cardiomyopathy.
      MicroRNAs (miRNAs) are a class of endogenous, small, noncoding RNAs that control the target gene expression at the posttranscriptional level. Increasing evidence indicates that miRNAs regulate pathophysiological processes such as cell differentiation, cell proliferation, apoptosis, and organ development.
      • Bartel D.P.
      MicroRNAs: genomics, biogenesis, mechanism, and function.
      • Plasterk R.H.
      Micro RNAs in animal development.
      Recent studies have described key roles of miRNAs in cardiovascular biology and heart disease.
      • van Rooij E.
      • Olson E.N.
      MicroRNAs: Powerful new regulators of heart disease and provocative therapeutic targets.
      • Divakaran V.
      • Mann D.L.
      The emerging role of microRNAs in cardiac remodeling and heart failure.
      A number of miRNAs have been shown to control the balance between differentiation and proliferation during cardiogenesis; and a variety of heart diseases, such as myocardial ischemia, cardiac fibrosis, cardiac arrhythmias, and heart failure, have been related to aberrant expression of miRNAs.
      • Hu S.
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      MicroRNA-210 as a novel therapy for treatment of ischemic heart disease.
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      • Marshall W.S.
      • Hill J.A.
      • Olson E.N.
      Dysregulation of microRNAs after myocardial infarction reveals a role of miR-29 in cardiac fibrosis.
      • Yang B.
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      • Xu C.
      • Bai Y.
      • Wang H.
      • Chen G.
      • Wang Z.
      The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2.
      In animal models of hypertrophy, whole arrays of miRNAs have been reported to be dysregulated with respect to the normal heart, thus indicating their common roles in hypertrophy pathogenesis.
      • Cheng Y.
      • Ji R.
      • Yue J.
      • Yang J.
      • Liu X.
      • Chen H.
      • Dean D.B.
      • Zhang C.
      MicroRNAs are aberrantly expressed in hypertrophic heart: Do they play a role in cardiac hypertrophy?.
      • van Rooij E.
      • Sutherland L.B.
      • Liu N.
      • Williams A.H.
      • McAnally J.
      • Gerard R.D.
      • Richardson J.A.
      • Olson E.N.
      A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure.
      Nevertheless, the role of miRNAs and their signaling pathways in regulating diabetes-induced cardiomyocyte hypertrophy remain largely unknown.
      Diabetic cardiomyopathy occurs independently of coronary artery disease and hypertension, and is a common complication of diabetes.
      • Rubler S.
      • Dlugash J.
      • Yuceoglu Y.Z.
      • Kumral T.
      • Branwood A.W.
      • Grishman A.
      New type of cardiomyopathy associated with diabetic glomerulosclerosis.
      • Hayat S.A.
      • Patel B.
      • Khattar R.S.
      • Malik R.A.
      Diabetic cardiomyopathy: mechanisms, diagnosis and treatment.
      Structural and functional changes in the diabetic heart are characterized by left ventricular hypertrophy (LVH), myocardial fibrosis, diastolic left ventricular dysfunction, and systolic dysfunction.
      • Khavandi K.
      • Khavandi A.
      • Asghar O.
      • Greenstein A.
      • Withers S.
      • Heagerty A.M.
      • Malik R.A.
      Diabetic cardiomyopathy—a distinct disease?.
      These pathogenetic changes lead to cardiomyocyte cell loss and reactive cellular hypertrophy, which is the leading cause of diabetes-related morbidity and mortality worldwide. Various molecular pathways and the regulators of hypertrophic response that are responsible for the control of cardiac hypertrophy in diabetes include Rac1,
      • Li J.
      • Zhu H.
      • Shen E.
      • Wan L.
      • Arnold J.M.
      • Peng T.
      Deficiency of rac1 blocks NADPH oxidase activation, inhibits endoplasmic reticulum stress, and reduces myocardial remodeling in a mouse model of type 1 diabetes.
      p300, vasoactive factors, redox-sensitive transcription factors,
      • Feng B.
      • Chen S.
      • Chiu J.
      • George B.
      • Chakrabarti S.
      Regulation of cardiomyocyte hypertrophy in diabetes at the transcriptional level.
      TGF1 cascade, insulin and insulin-like growth factor–I (IGFI) signaling, and calcineurin-NFAT3 hypertrophic pathway.
      • Kuo W.W.
      • Chung L.C.
      • Liu C.T.
      • Wu S.P.
      • Kuo C.H.
      • Tsai F.J.
      • Tsai C.H.
      • Lu M.C.
      • Huang C.Y.
      • Lee S.D.
      Effects of insulin replacement on cardiac apoptotic and survival pathways in streptozotocin-induced diabetic rats.
      A recently published study by Feng et al
      • Feng B.
      • Chen S.
      • George B.
      • Feng Q.
      • Chakrabarti S.
      miR133a regulates cardiomyocyte hypertrophy in diabetes.
      firmly supports the hypothesis that miRNAs plays a key role in cardiomyocyte hypertrophy in diabetes. These investigators confirmed the action of miR-133a by discovering that down-regulation of miR-133a mediates diabetes-induced cardiomyocyte hypertrophy in mice, which resulted in upregulation of the miR-133a regulatory targets SGK1 and IGFR1, two transcription factors associated myocardial hypertrophy. Nevertheless, the signaling pathways that regulate the expression of miR-133a during diabetes-induced cardiomyocyte hypertrophy remain unknown. Thus, further investigation is needed to ascertain whether more novel miRNAs and related networks are associated with cardiac hypertrophy in diabetes.
      The primary aim of the present study was twofold: to elucidate the expression of miRNA in cardiac hypertrophy induced by hyperglycemia in mice, and to further understand the role of miRNAs as they relate to diabetic cardiomyopathy. A microarray technique was used to present a comprehensive picture of the expression of miRNAs, and a prediction database was applied to select the target genes of the altered miRNAs. In addition we investigated the functional information of these miRNAs and the related regulatory networks by gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

      Materials and Methods

      Animal Studies

      Experiments were performed using age- and weight-matched C57/BL6 male mice (8 weeks old, 23 to 25 g body weight; Sippr-bk Lab Animal Ltd., Shanghai, China). Before and during treatment, all animals were housed in a climate-controlled room with a 12-hour light/dark cycle. Animals were fed a standard diet and allowed tap water ad libitum. All procedures were performed in accordance with National Institutes of Health (NIH) guidelines (no. 85-23, revised 1996) for the care and use of experimental animals.
      Diabetes was induced by a single injection of streptozotocin (STZ, 150 mg/kg i.p) dissolved in 0.1 mol/L citrate buffer, pH 4.3 (Sigma, St. Louis, MO). Nondiabetic control mice received the citrate buffer only. Three day after the injection of STZ or vehicle, blood glucose levels were measured using the Optium Xceed Diabetes Monitoring System (Abbott Diabetes Care, Alameda, CA). Blood glucose content of ≥18.6 mmol/L was chosen as indicating diabetes in the present study.
      • Shen E.
      • Li Y.
      • Shan L.
      • Zhu H.
      • Feng Q.
      • Arnold J.M.
      • Peng T.
      Rac1 is required for cardiomyocyte apoptosis during hyperglycemia.

      Cell Culture

      Primary neonatal rat myocytes were isolated from newborn Sprague-Dawley rat heart ventricles by collagenase digestion and cultured as described previously.
      • Feng B.
      • Chen S.
      • Chiu J.
      • George B.
      • Chakrabarti S.
      Regulation of cardiomyocyte hypertrophy in diabetes at the transcriptional level.
      In brief, isolated cardiomyocytes were plated onto cell culture dishes at a density of 3.0 × 104 cells/cm2 and were maintained for 24 hours in Dulbecco's modified Eagle's medium (DMEM). The cells were serum starved 24 hours before use for the experiments. All experiments were performed after 48 hours of incubation with 5.5 mmol/L (control; NG) or 25 mmol/L D-glucose (high glucose; HG). A 25-mmol/L quantity of L-glucose was used as an osmotic control. The experiments were repeated with at least three different cardiomyocyte cultures.
      In the experiments, to inhibit p38 MAPK, ERK1/2, and JNK, cardiomyocytes were pretreated with or without 5 μmol/L SB-203580, PD-98059 or SP-600125 for 30 minutes and then exposed to 5.5 or 25 mmol/L glucose for 48 hours.

      Cardiac Hypertrophy and Cardiomyocyte Cell Size Analysis

      At 8 weeks after diabetes was induced, the left ventricle was rinsed in PBS buffer and weighted to calculate the ratio of heart weight to body weight (HW/BW). Cardiac hypertrophy was analyzed by histology. Briefly, LV samples were fixed in 4% paraformaldehyde for 12 hours at room temperature and embedded in paraffin. After that, 5-μm-thick sections were stained with hematoxylin and eosin (H&E) and cardiomyocyte size was measured using NIH ImageJ software on micrographs. Approximately 100 randomly chosen cardiomyocytes from each group (n = 5) were analyzed to measure the cross-sectional cardiomyocyte area.
      • Barbosa M.E.
      • Alenina N.
      • Bader M.
      Induction and analysis of cardiac hypertrophy in transgenic animal models.
      • Zou Y.
      • Hiroi Y.
      • Uozumi H.
      • Takimoto E.
      • Toko H.
      • Zhu W.
      • Kudoh S.
      • Mizukami M.
      • Shimoyama M.
      • Shibasaki F.
      • Nagai R.
      • Yazaki Y.
      • Komuro I.
      Calcineurin plays a critical role in the development of pressure overload-induced cardiac hypertrophy.
      Cardiomyocyte cell surface area was determined to assess cellular hypertrophy.
      • Feng B.
      • Chen S.
      • George B.
      • Feng Q.
      • Chakrabarti S.
      miR133a regulates cardiomyocyte hypertrophy in diabetes.
      Cells were visualized with a Nikon (Tokyo, Japan) microscope and images were captured at 40× magnification. At least 100 cells from randomly selected fields in one well were examined.

      Real-Time RT-PCR

      Total RNA was extracted from mouse LV tissues using the TRIzol reagent (Invitrogen) following the manufacturer's instructions. Only the highly pure RNAs (ratio of 260/280 > 1.8 and 260/230 > 1.8) were used for downstream assay. Real-time RT-PCR for atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), β-myosin heavy chain (β-MHC), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were performed using the primers as follows: ANP (GenBank number: NM_008725.2) forward, 5′- TGACAGGATTGGAGCCCAGAG-3′; reverse, 5′- AGCTGCGTGACACACCACAAG-3′; BNP (GenBank number: NM_008726.4) forward, 5′-ATCGGATCCGTCAGTCGTTTG-3′; reverse, 5′-CCAGGCAGAGTCAGAAACTGGAG-3′; β-MHC (GenBank number: NM_080728.2) forward, 5′-TAACCCGAGGCAAGCTCACA-3′; reverse, 5′-CACAATCATGCCGTGCTGAC-3′; GAPDH (GenBank number: NM_008084.2) forward, 5′- TGTGTCCGTCGTGGATCTGA-3′; reverse, 5′-TTGCTGTTGAAGTCGCAGGAG-3′. The neonatal rat cardiomyocytes were cultured in DMEM. miR-373 levels were detected by real-time quantitative PCR. The sequences of primers, which were designed for miR-373 and U6 (for normalization), to analyze their expression, were as follows: miR-373 stem-loop primer 5′-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACACACC-3′; miR-373 forward, 5′-GTGCTTCGATTTTGGGG-3′; miR-373 reverse, 5′-GTGCAGGGTCCGAGGT-3′; U6 forward, 5′-CTCGCTTCGGCAGCACA-3′; and U6 reverse transcript and reverse, 5′-AACGCTTCACGAATTTGCGT-3′. Relative gene expression levels were calculated by the 2−ΔΔCT method.
      • Livak K.J.
      • Schmittgen T.D.
      Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method.

      Microarray Analysis and Target Prediction

      miRNAs were purified from total RNA by using the mirVana miRNA Isolation Kit (Ambion, Austin, TX). Purification of miRNA from three cardiac tissues in each group was labeled and used for miRNA expression analysis by using the CapitalBio Mammalian miRNA Array V4.0 (CapitalBio Corp.) containing 1320 probes including human, mouse, and rat mature miRNAs. Differentially regulated miRNAs were defined as those with either _0.5- or 1.5-fold changes in expression for both arrays from diabetic or nondiabetic mouse heart. The normalized microarray data were validated by using real-time PCR. The commonly used and accurate Sanger miRNAs database (http://microrna.sanger.ac.uk) was used to predict potential miRNA targets. The top 25% of miRNA targets that had been assigned the highest numbers of miRNAs interaction sites were collected.

      GO Category and Pathway Analysis

      The top 25% of miRNA targets were subjected to GO term analysis, which was applied to organize genes into hierarchical categories on the basis of biological process, molecular function, and subcellular localization.
      • Sam L.T.
      • Mendonca E.A.
      • Li J.
      • Blake J.
      • Friedman C.
      • Lussier Y.A.
      PhenoGO: an integrated resource for the multiscale mining of clinical and biological data.
      Generally, a two-sided Fisher's exact test and χ2 test were used to classify the GO category, and the false discovery rate (FDR) was calculated to correct the P value. We computed P values for all of the different genes in all GO biological categories; the threshold of significance was defined as P < 0.01 and FDR < 0.05. Enrichment provides a measure of the significance of the function: As the enrichment increases, the corresponding function is more significant. Within the significant category, the enrichment Re was given by the following: Re (n/n)/(N / N), where nf is the number of differential genes within the particular category, n is the total number of genes within the same category, Nf is the number of differential genes in the entire microarray, and N is the total number of genes in the microarray.
      Meanwhile, the top 25% of miRNA targets were collected, and subjected to KEGG pathway annotation. Similarly, pathway analysis was used to determine the significant pathway of the differential genes. Fisher's exact test and χ2 test were used to select t significant pathway, and the threshold of significance was defined as P < 0.01 and FDR < 0.05. The enrichment Re was calculated like the equation above. Furthermore, the network of miRNA-mRNA interaction, representing the critical miRNAs and their targets, was established based on the integration of GO function and KEGG analysis findings.

      miRNA Mimic Transfection

      The primary neonatal rat cardiomyocytes were seeded in antibiotic-free medium for 24 hours before transfection. Lipofectamine 2000 (Invitrogen) was used to transfect cells with miR-373 (20 nmol/L) mimic (Dharmacon RNA Technologies) for 48 hours according to the manufacturer's instructions. miRIDIAN miRNA mimic negative controls (Dharmacon RNA Technologies) were used for control transfections. Cells were collected after 48 hours of miRNA mimic transfections for quantification of miRNA and protein expression.

      Western Blot Analysis Significant Pathways

      Total proteins isolated from LV tissues or cultured cardiomyocytes were prepared by standard procedures and quantified by the bicinchoninic acid (BCA) protein assay (Pierce, USA). A total of 30 μg protein in each sample was subjected to 10% SDS-PAGE gel by electrotransfer onto polyvinylidene difluoride membranes (Millipore, USA). The expression or phosphorylation of p38, ERK1/2, and JNK were determined by using specific antibodies against total-p38, -ERK1/2, -JNK and phospho-p38, -ERK1/2, and -JNK, respectively. The primary antibody against MEF2C was used to analyze MEF2C protein expression. After scanning, results were quantified by the NIH ImageJ program.

      Statistical Analysis

      All experimental data are expressed as mean ± SD and were analyzed by Wilcoxon Mann–Whitney method and Kruskal–Wallis test. A P value of 0.05 or less was considered significant.

      Results

      Hyperglycemia Induces Cardiac Hypertrophy and Neonatal Rat Cardiomyocyte Hypertrophy

      At 72 hours after STZ injection, blood glucose was elevated to 24.0 ± 3.0 mmol/L in diabetic mice versus 8.3 ± 0.6 mmol/L in the C57BL6 controls. Throughout the 8-week study period, mice with STZ-induced diabetes displayed severe hyperglycemia. At the end of 8 weeks after diabetes was induced, increased LVHW/BW ratios were observed in diabetic mice (Figure 1A). At the same time, histological analysis showed that the cross-sectional area of cardiomyocytes increased in the diabetic heart (Figure 1, B and C). Furthermore, ANP, BNP, and β-MHC, markers of cardiac hypertrophy, were upregulated in diabetic hearts as assessed by real-time RT-PCR (Figure 1D). Further in vitro investigation showed that high glucose increased the size of cultured cardiomyocytes exposed to 25 mmol/L glucose for 48 hours (Figure 1E).
      Figure thumbnail gr1
      Figure 1Glucose causes cardiac hypertrophy of diabetic mice and cardiomyocytes hypertrophy of neonatal rat. A: Heart weight to body weight ratios (HW/BW) from diabetic mice (n = 15) and nondiabetic mice (n = 10). B: Morphological analysis of hearts from diabetic mice and nondiabetic mice. Scale bar = 1 mm (top). Hematoxylin-eosin–stained micrographs showing transverse sections of left ventricular myocardium. Original magnification, ×40. Scale bar = 25 μm (bottom) C: Measurement of cardiomyocyte cross-sectional area sampled from heart sections (n = 5 for each group). D: mRNA expression of ANP, BNP, and β-MHC in cardiac hypertrophy induced by STZ (n = 5). E: Micrograph images of cultured rat neonatal myocytes treated with 25 mmol/L glucose (HG), 25 mmol/L L-glucose (osmotic), or 5 mmol/L glucose (NG) after 48 hours (left panels). Cell surface areas of more than 100 individualized cells per condition were measured using ImageJ software (right panel). *P < 0.05 compared with control or NG.

      Altered Expression of miRNAs and Their Target Genes in the Diabetic Heart

      Using miRNA microarray analysis, we evaluated miRNA expression profiles of three diabetic heart samples and three nondiabetic heart samples (fold change: _0.5- or 1.5; false discovery rate: ≤0.05). We identified 19 miRNAs that were deregulated greater than twofold, and 16 were further verified by real-time PCR. Overall, miR-195, miR-199a-3p, miR-700, miR-142-3p, miR-24, miR-21, miR-221, miR-499-3p, miR-208a, and miR-705 have often been found to be upregulated, whereas miR-29, miR-1, miR-373, miR-143, miR-20a, and miR-220b have been found to be down-regulated in STZ-induced diabetes when compared with controls. By using the Sanger database, 3212 target genes of the 16 changed miRNAs were collected.

      GO Function and KEGG Pathway Enrichments

      To gain a better understanding of the functional roles of the predicted miRNAs target genes and related regulatory network, we looked for target enrichment in GO and KEGG. On the basis of the top 25% miRNA targets, 31 GO terms were found to be involved in the diabetic cardiomyopathy (Table 1). The five high-enrichment GOs were insulin-like growth factor receptor signaling, JNK cascade, leukocyte adhesive activation, transmembrane receptor protein serine/threonine kinase signaling, MAPKKK cascade, and collagen fibril organization. They were involved in proliferative, metabolic, extracellular matrix, adhesive, apoptosis, signal transduction, and heart physiology. KEGG pathway analysis of the top 25% of the predicted targets that were assigned the highest numbers of miRNA target sites revealed 26 KEGG pathways enriched in diabetic cardiomyopathy (Figure 2). Many of these signaling pathways, such as MAPK, Wnt, TGF-β, VEGF, JAK/STAT, and mTOR have been shown to participate in the development of cardiac hypertrophy (Table 2). Among the list of high-enrichment KEGG pathways of these miRNA-targeted genes, MAPK signaling pathway was significantly prominent. A similar phenomenon was also observed in GO analysis.
      Table 1MicroRNA Targets Significant in Hearts of STZ–Induced Diabetic Mice, by GO Identification Number and Name
      GO_idGO_nameP valueFDREnrichment
      GO:0048009Insulin-like growth factor receptor signaling pathway0.0003033840.00302866512.295015576
      GO:0007254JNK cascade2.13953E-050.00031218211.750222045
      GO:0050902Leukocyte adhesive activation0.0003721060.00362729810.07202492
      GO:0007178Transmembrane receptor protein serine/threonine kinase signaling pathway2.8093E-065.00967E-057.747711479
      GO:0000165MAPKKK cascade0.0001081990.0012460346.956866934
      GO:0030199Collagen fibril organization5.22196E-068.91667E-055.722741433
      GO:0008286Insulin receptor signaling pathway3.26149E-088.50239E-074.864330218
      GO:0008016Regulation of heart contraction2.53336E-050.00035954.196677051
      GO:0007265Ras protein signal transduction3.02529E-101.15234E-084.046795728
      GO:0007179Transforming growth factor beta receptor signaling pathway5.34664E-069.11004E-053.934384735
      GO:0051056Regulation of small GTPase mediated signal transduction1.66201E-095.593E-083.896914404
      GO:0001666Response to hypoxia7.90691E-092.32412E-073.456086983
      GO:0030036Actin cytoskeleton organization2.41342E-131.35427E-113.324334068
      GO:0043065Positive regulation of apoptosis1.89632E-063.46985E-053.264082151
      GO:0035023Regulation of Rho protein signal transduction1.81537E-074.4.03059E-063.178066116
      GO:0030308Negative regulation of cell growth5.99755E-081.50693E-063.175863714
      GO:0007160Cell-matrix adhesion1.35546E-050.0.0002107463.106631064
      GO:0007243Protein kinase cascade9.54633E-082.2.3098E-063.042590862
      GO:0007264Small GTPase mediated signal transduction8.12556E-197.5993E-172.988731431
      GO:0016055Wnt receptor signaling pathway5.97358E-102.2.17568E-082.932904984
      GO:0006006Glucose metabolic process0.0001912710.0.0019876012.877721406
      GO:0007010Cytoskeleton organization5.58167E-071.1.12111E-052.710353929
      GO:0007507Heart development1.51048E-050.0.0002316962.673988917
      GO:0006950Response to stress8.30749E-092.42587E-072.628016211
      GO:0006979Response to oxidative stress2.57902E-050.0003648382.538312732
      GO:0001558Regulation of cell growth1.62148E-063.00709E-052.502837518
      GO:0008283Cell proliferation6.44871E-154.17534E-132.426344333
      GO:0042981Regulation of apoptosis5.36115E-050.0006976362.304057989
      GO:0006917Induction of apoptosis5.72349E-071.14703E-052.284267256
      GO:0007155Cell adhesion1.1192E-191.1.24009E-172.229968617
      GO:0055114Oxidation reduction6.80721E-050.0008503891.515909961
      id, identification number; MAPKKK, mitogen- activated protein kinase kinase kinase; STZ, streptozotocin.
      Figure thumbnail gr2
      Figure 2Pathway analysis based on miRNA-targeted genes, which showed significant pathways targeted by aberrantly expressed miRNAs. The vertical axis is the pathway category; the horizontal axis is the enrichment of pathways.
      Table 2Significant Pathways That Are Enriched with Target Genes of MicroRNA
      Path_nameP valueFDRTarget genes
      MAPK signaling pathway1.13E-325.557E-31Pla2g4b, NF1,Dusp3, Dusp4, PLA2G4A, MAPK7, MAP2K1, NLK, GNG12, ELK4, ACVR1B, RAPGEF2, MAPK8IP3, CACNB1, MAPK8IP1, NTF3, MAPK3, CRKL, MRAS, DUSP16, TAOK2, NTRK2, MAP3K14, TAOK1, Cacng2, MAP3K7IP2, FGF1, TAOK3, FASLG, DUSP5,RAF1, RPS6KA5, DUSP2, EVI1, MAP3K11, FGF2, KRAS, RPS6KA1, PDGFA, MAP3K3, TGFB3, PPM1A, CACNG3, PPP3R1, RASGRP4, MAP3K1, RASGRP1, PPP3CB, PPM1B, CACNA2D3, MEF2C, STK4, CACNA1E, MAP3K7, CDC42, PAK1, JUND, MAP4K3, CACNA1C, Rasgrf1, Akt1, TNFRSF1A, RAC1, MAP2K3, CHP, MAP3K5, Hspa1l, PPP3CA, MAP3K4, TGFBR1, RAP1A, AKT3, Hspa2, BDNF, GNA12, RASA1, MAPK9, FOS,Srf, RAP1B, MAP2K6, CRK, MAP2K4, FGFR3, Nfatc2, FGF7, MAPK10, PDGFRA, IL1R1, ACVR1C
      Wnt signaling pathway8.141E-212.002E-19PPP2R1A, FBXW11, CTBP2, Csnk1e, Nkd2, NLK, VANGL1, BTRC, NFATC3, Ctbp1, WNT7A, LEF1, CCND1, Crebbp, CSNK1A1, FZD7, SFRP1, SFRP2, ROCK2, SMAD4, LRP6, PPP2R5C, PLCB1, Camk2b, SMAD3, PPP3R1, TBL1X, DAAM1, NFAT5, PPP3CB, WNT1, CCND2, MAP3K7, SMAD2, Camk2a, PPP2R5E, APC,SIAH1, RAC1, TCF7, CHP, PPP3CA, CSNK2A2, FZD9, MAPK9, WNT3A, FOSL1, FZD10, Nfatc2, Ppp2ca, MAPK10, PRICKLE2, AXIN2
      Focal adhesion3.979E-207.369E-19MET, PDPK1, Ppp1cc, ZYX, COL3A1, MAP2K1, VEGFA, PXN, CHAD, ITGA11, COL1A2, CRKL, COL1A1, CCND1, COL4A1, TNXB, VASP, MAPK3, RAF1, ROCK2, PDGFA, Rasgrf1, COL11A2, PARVA, CAV2, PAK4, ITGB8, CCND2, IGF1, ITGAV, Ilk, PDGFC, PAK1, CDC42, ITGA2, RAC1, Akt1, COL6A3, RAP1A, PPP1R12A, PIK3R1, COL5A3, ITGA3, COL5A2, FLT1, VAV1, AKT3, PTEN, RAP1B, MAPK9, LAMC1, BCL2, COL2A1, FN1, CRK, RELN, MYLK, ITGA6, SLK, MAPK10, PDGFRA
      Regulation of actin cytoskeleton2.714E-153.086E-14Pfn1, PDGFRA, GIT1, WASL, TMSL, CSK, Ppp1cc, GNG12, MYH9, PIP5K1A, MRAS, PXN, ITGA11, LIMK1, FGF1, CRKL, FGF2, MAP2K1, ROCK2, Chrm2, IQGAP2, PDGFA, ARHGEF12, RAF1, SSH2, MAPK3, PAK4, BAIAP2, KRAS, ITGB8, TMSB4X, ITGAV, FN1, APC, PDGFC, PAK1, ENAH, MYH10, ITGA2, CDC42, ARHGEF7, PPP1R12A, GNA12, ITGA3, RAC1, PIK3R1, Pip5k3, VAV1, PFN2, CRK, MYLK, FGFR3, FGF7, CFL2, SLC9A1, ITGA6
      TGF-beta signaling pathway8.813E-147.88E-13FST, ACVR1C, Nog, PPP2R1A, BMPR1A, ACVR1B, SMURF2, SMAD7, MAPK3, Crebbp, SMAD5, SMURF1, E2F5, ROCK2, SMAD4, RBL1, TGFB3, INHBB, SMAD3, LEFTY1, ACVR2A, SMAD2, ACVR2B, GDF6, PITX2, SP1, TGFBR1, Acvrl1, ACVR1, Id1, Ppp2ca, CHRD
      GnRH signaling pathway4.029E-132.91E-12CALM3, MMP14, MAPK10, Pla2g4b, MAPK7, PLA2G4A, CALM2, ITPR3, MAP2K1, CALM1, ITPR1, MAP3K3, Adcy2, PLCB1, MAP3K1, Camk2b, RAF1, MAPK3, ADCY9, Adcy3, ADCY1, KRAS, Camk2a, CACNA1C, MAP2K3, MAP3K4, Ptk2b, CDC42, Gna11, MAP2K6, MAP2K4, GNAQ, HBEGF, MAPK9
      Phosphatidylinositol signaling system4.489E-133.153E-12PIK3C2A,CALM2,ITPR3,PIP5K1A,OCRL, CALM1, ITPR1, Impa2, PLCB1, PI4KA, PI4KB, IPPK,CDS2, DGKB, INPP5F, ITPKB, DGKD, PIK3R1, INPP5B, PIB5PA, DGKG, INPP5A, PLCG1, PTEN, ITPK1, Pip5k3, DGKH, CALM3, SYNJ1
      Adherens junction4.639E-122.535E-11ACVR1C, WASL, MET, NLK, ACVR1B, CTNND1, PTPN1, LEF1, PTPRF, CTNNA1, Crebbp, MAPK3, SMAD4, Snai1, SMAD3, BAIAP2, WASF3, MAP3K7, SMAD2, PVRL2, CDC42, TCF7, RAC1, CSNK2A2, TGFBR1, SSX2IP, YES1, SLK
      T cell receptor signaling pathway7.094E-113.668E-10SLK, NCK2, MAP2K1, CD4, NFATC3, MAP3K14, CBLB, CD28, CBL, RAF1, MAPK3, RASGRP1, KRAS, PAK4, PPP3R1, NFAT5, PPP3CB, MAP3K7, PAK1, CDC42, Akt1, CHP, PPP3CA, PIK3R1, PLCG1, VAV1, FOS, Csf2, AKT3, MAPK9, DLG1, Nfatc2
      ErbB signaling pathway1.344E-106.612E-10NRG2, CDKN1B, GAB1, MAP2K1, NCK2, MAPK3, ABL1, CRKL, CBLB, RAF1, CBL, ABL2, KRAS, FRAP1, Camk2b, ERBB4, PAK4, Camk2a, PAK1, Akt1, PIK3R1, AKT3, PLCG1, MAPK9, CRK, MAP2K4, HBEGF, MAPK10
      Chemokine signaling pathway8.412E-103.575E-09GNB5, WASL, CSK, Ccl3, MAP2K1, GNG12, Gip, PXN, STAT3, CX3CR1, MAPK3, ADRBK2, GNG2, CRKL, GRK6, CX3CL1, ROCK2, RAF1, PREX1, PLCB1, KRAS, GNAI3, Adcy2, Stat1, GNAI2, ADCY9, Adcy3, ADCY1, CXCL11, CDC42, PAK1, CCL1, RAC1, Akt1, CCL5, PIK3R1, Ptk2b, RAP1A, AKT3, VAV1, RAP1B, CRK, GNB2
      Insulin signaling pathway1.018E-094.185E-09CALM3, MAPK10, PDPK1, SLC2A4, Ppp1cc, Hk3, CALM2, PTPN1, EIF4E2, PRKAR2A, PTPRF, CRKL, MAP2K1, CBLB, CALM1, CBL, PPP1R3A, FRAP1, FLOT2, RHEB, PPP1R3D, RAF1, Hk1, MAPK3, PPP1R3B, KRAS, TRIP10, Akt1, FOXO1, PIK3R1, PPARGC1A, EIF4E, MAPK9, CRK, AKT3
      Vascular smooth muscle contraction8.36E-092.757E-08Pla2g4b, Ppp1cc, PLA2G4A, CALM2, MAP2K1, ITPR3, MAPK3, ROCK2, CALM1, RAF1, ITPR1, PLCB1, CALD1, PRKCE, Adcy2, ARHGEF12, ARHGEF11, ADCY9, Adcy3, ADCY1, KCNMA1, CACNA1C, PPP1R12B, PPP1R12A, GNA12, Gna11, PRKG1, GNAQ, MYLK, CALM3
      ECM-receptor interaction8.649E-092.836E-08HSPG2,COL3A1,CHAD,ITGA11,COL1A2,COL1A1, COL4A1, TNXB, SDC4, SDC2, SDC1, COL11A2, CD44, ITGB8, ITGAV, ITGA2, COL6A3, COL5A3, ITGA3, COL5A2, LAMC1, COL2A1, FN1, RELN, ITGA6
      mTOR signaling pathway1.405E-084.343E-08PDPK1, ULK1, CAB39, VEGFA, MAPK3, EIF4E2, RICTOR, STK11, RPS6KA1, FRAP1, RHEB, DDIT4, IGF1, Akt1, PIK3R1, AKT3, HIF1A, EIF4B, EIF4E
      Notch signaling pathway1.46E-084.488E-08CTBP2, Jag2, NOTCH3, NOTCH1, JAG1, Ctbp1, Crebbp, NUMB, Ncor2, DLL1, DLL4, Hes5, KAT2B, HN1, MAML3, MAML1, RBPJ, Psen2
      VEGF signaling pathway1.258E-073.496E-07Pla2g4b, PTGS2, PLA2G4A, MAP2K1, VEGFA, PXN, NFATC3, MAPK3, RAF1, KRAS, PPP3R1, NFAT5, PPP3CB, CDC42, RAC1, Akt1, CHP, PIK3R1, PPP3CA, AKT3, PLCG1, Nfatc2
      Cell cycle2.445E-076.165E-07CCNE1, CDC25A, CDKN1B, CDC23, YWHAB, E2F3, YWHAE, CCND1, YWHAG, ABL1, Crebbp, YWHAH, MYT1, YWHAQ, SMAD4, CDC14A, RBL1, CDK6, TGFB3, SMAD3, WEE1, CCND2, SMAD2, Ccnb2, CDKN1C, YWHAZ, RB1, CHEK1
      Circadian rhythm, mammal2.809E-066.092E-06CLOCK, CRY2, Csnk1e, NR1D1, BHLHE41, PER2, PER1, ARNTL
      Glycerophospholipid metabolism2.906E-066.276E-06CHKA, GPD1L, PISD, CDS2, AGPAT3, AGPAT1, DGKB, ETNK1, GPD2, DGKD, DGKG, LYPLA2, CHPT1, Ache, PHOSPHO1, DGKH, Pgs1, Pla2g4b, PLA2G4A
      Cytokine–cytokine receptor interaction5.155E-059.309E-05BMPR1A, Cd40, LEPR, Ccl3, LIFR, Tnfrsf25, VEGFA, ACVR1B, PRLR, LTB, CX3CR1, CX3CL1, FASLG, LIF, IL13RA1, GHR, TNFRSF21, PDGFA,TGFB3, KITLG, INHBB, CNTFR, CXCL11, ACVR2A, ACVR2B, CCL1, PDGFC, TNFRSF1A, CCL5, NGFR, CNTF, TGFBR1, TNFRSF19, Acvrl1, FLT1, Csf2, ACVR1, EDA, IL1R1, PDGFRA, MET
      Jak–STAT signaling pathway6.844E-050.0001202PIAS3, STAM, LEPR, LIFR, PRLR, STAT3, CCND1, PIAS1, LIF, Crebbp, IL13RA1, CBLB, CBL, GHR, Stat1, CNTFR, CCND2, CNTF, Akt1, PIK3R1, Csf2, PIM1, AKT3, SPRY4, SPRED2, SPRY2, SPRED1, SPRY1
      Toll-like receptor signaling pathway7.645E-050.0001326Cd40, Ccl3, IRAK1, MAP2K1, MAP3K7IP2, MAPK3, Stat1, CXCL11, MAP3K7, MAP2K3, CCL5, RAC1, Akt1, PIK3R1, FOS, MAP2K6, AKT3, MAPK9, MAP2K4, TIRAP, MAPK10
      p53 signaling pathway0.00015920.0002601BAI1, CCNE1, CCND1, IGFBP3, SESN1, PPM1D, CDK6, PMAIP1, CCND2, RCHY1, IGF1, Ccnb2, SIAH1, Lrdd, PTEN, CHEK1
      Cardiac muscle contraction0.00026020.0004089UQCRB, Atp1a3, CACNB1, ATP2A2, Uqcrc2, Cacng2, CACNG3, Cox6a1, CACNA2D3, Tnnc1, CACNA1C, TPM3, SLC8A1, SLC9A6, ATP1A1, SLC9A1, Myl4
      PPAR signaling pathway0.00056430.0008409PDPK1, ACOX1, SCD, APOA5, Angptl4, PPARA, ACSL1, Ilk, ANGPTL2, Cyp27a1, LPL, SCD5, ACSL4, NR1H3, Rxra
      ECM, extracellular matrix; MAPK, mitogen-activated protein kinase; PPAR, peroxisome proliferator-activated receptor; TGF, transforming growth factor; VEGF, vascular endothelial growth factor.
      In addition to providing the interactions of miRNAs and target genes, the common targets of GO analysis and KEGG pathway were filtered out, miRNAs–gene network analysis integrated changed miRNAs, and 192 critical mRNAs were further generated (Figure 3). Intriguingly, the most overrepresented miRNA targets belonged to the MAPK signaling pathway (Figure 4), which is known to be involved in the hypertrophic growth in response to overpressure by affecting the activities of the three best-characterized MAPKs including extracellular signal-regulated kinase (ERK1/2), c-Jun NH2-terminal kinase (JNK), and p38 mitogen-activated protein kinase (p38). These pathway analyses illustrate some of the possible roles of the highly expressed miRNAs in cardiac hypertrophy of diabetic mice.
      Figure thumbnail gr3
      Figure 3MicroRNA–gene network. Square represents miRNAs; circle represents target genes; straight line represents miRNA–gene relationship. The size of the square represents the degrees of miRNAs; larger degrees have larger numbers of target genes.
      Figure thumbnail gr4
      Figure 4The interaction between MAPK signaling pathway–related genes and miRNAs. Square represents miRNAs; circle represents target genes; straight line represents miRNAs-MAPK signaling pathway–related gene relationship.

      Experimental Verification the MAPK Signaling Pathway

      To further investigate whether MAPK signaling pathway mediates cardiomyocyte hypertrophy induced by hyperglycemia, we investigated the role of JNK, ERK1/2, and p38 MAPK in the heart in diabetes and in the cardiomyocytes exposed high glucose. Our results showed that diabetes in the heart causes significant upregulation of JNK, ERK1/2, and p38 (Figure 5A) in association with morphometrically demonstrable cardiomyocyte hypertrophy and ANP, BNP, and β-MHC upregulation (Figure 1). Exposure of cardiomyocytes to 25 mmol/L glucose for 48 hours showed similar upregulation of JNK, ERK1/2, and p38 MAPK protein levels compared with 5.5 mmol/L glucose (NG) and 25 mmol/L L-glucose (Figure 5B).
      Figure thumbnail gr5
      Figure 5Activation of JNK, ERK1/2, and p38 MAPK in cardiomyocyte hypertrophy induced by hyperglycemia. A: Western blot analysis of JNK, ERK1/2, and p38 MAPK protein levels in cardiac tissue of diabetic mice (upper panels). Results are shown for 3 mice in each group. B: Western blot analysis of ERK1/2, JNK, and P38 MAPK in neonatal rat cardiomyocytes (upper panels). Data are mean ± SD from at least three different cell cultures. For A and B, bar graph (lower panels) shows expression ratio of phospho-JNK over total JNK, phospho-ERK over total ERK, and phospho-p38 over total p38, respectively. *P < 0.05 compared with control or NG.

      miR-373 Mediates Glucose-Induced Cardiomyocyte Hypertrophy via Target Gene MEF2C

      To gain insight into the possible functional roles of miRNAs in diabetic cardiomyopathy, miR-373 levels in neonatal rat cardiomyocytes were modulated by transfecting the cells with miRIDIAN miR-373 mimic. A negative control miRNAs mimic was used as control. Transfection with miR-373 mimic in glucose-exposed cells significantly increased the expression of miR-373 and decreased the cell size as measured by cell surface area (Figure 6, A and B). We also observed that the protein level of MEF2C was reduced when overexpressing miR-373 (Figure 6C). The results suggested that MEF2C was a target gene of miR-373.
      Figure thumbnail gr6
      Figure 6Neonatal rat cardiomyocytes were exposed to high glucose and transfected with miR-373 mimic. A: miR-373 expression levels in cardiomyocytes 48 hours posttransfection were determined using real-time PCR. miRNA mimic negative controls were used for control transfections. *P < 0.05 compared with control-transfected cardiomyocytes. B: Overexpression of miR-373 decreased cell surface area as compared with HG. C: Western blot analysis of MEF2C in miR-373–overexpressing cardiomyocytes (upper panels). Quantitative analysis MEF2C protein levels by densitometry (lower panels). *P < 0.05 compared with HG; **P < 0.05 compared with NG.

      miR-373 Down-Regulation Is Dependent on p38 MAPK Pathway

      To understand the relationship between miR-373 and MAPK signaling pathway in cardiac myocytes, activation of p38 MAPK, ERK1/2, and JNK was evaluated by Western blot. As shown in Figure 7, high glucose significantly increased the phosphorylation levels of p38 MAPK, ERK1/2, and JNK, but miR-373 mimic had no effect on phospho-p38 MAPK, phospho-ERK1/2, and phospho-JNK expression. To further explore the functional role of p38 MAPK, ERK1/2, and JNK, we used SB-203580, PD-98059, and SP-600125 to test whether inhibition of p38 MAPK, ERK1/2, and JNK could influence the effect of miR-373 on cardiac hypertrophy and MEF2C expression. Of note, a p38 MAP kinase inhibitor SB203580 significantly decreased miR-373 levels and inhibited high-glucose–induced hypertrophy (Figure 8, A–C), whereas PD98059 or SP600125 had no effect on miR-373 levels and cell size (data not shown). Furthermore, administration of SB-203580 led to a reduction in MEF2C levels compared with those in the HG group (Figure 8D).
      Figure thumbnail gr7
      Figure 7Effect of miR-373 mimic on expression of JNK, ERK, and p38 MAPK. Representative Western blot of phospho-JNK and total JNK, phospho-ERK1/2 and total ERK1/2, and phospho-p38 and total p38 in myocytes after miR-373 mimic transfected (upper panels, A–C). For A–C, bar graph (lower panels) shows expression ratio of phospho-JNK over total JNK, phospho-ERK1/2 over total ERK1/2, and phospho-p38 over total p38, respectively. *P < 0.05 compared with control or NG.
      Figure thumbnail gr8
      Figure 8The effect of p38 inhibitor on cardiac myocyte hypertrophy, miR-373, and MEF2C expression. A: Cardiac myocyte size treated with or without p38 inhibitor in the presence or absence of high glucose. B: Quantitative analysis of cardiomyocyte surface area. C: Effect of p38 inhibitor on miR-373 expression levels in cardiomyocytes. D: Representative Western blot of MEF2C protein expression levels (upper panels). Quantitative analysis MEF2C protein levels by densitometry (lower panels). *P < 0.05 compared with HG; **P < 0.05 compared with NG.

      Discussion

      It is well demonstrated that miRNAs levels are altered in response to hypertrophic stimulation.
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      • Zou S.
      Methyl-CpG binding protein MBD2 is implicated in methylation-mediated suppression of miR-373 in hilar cholangiocarcinoma.
      At present, there is no report on how the miR-373 responds to diabetic cardiomyopathy. In our study, we found that miR-373 expression was decreased in the diabetic heart. To further explore the function of miR-373 in the regulation of glucose-induced cardiomyocyte hypertrophy, miR-373 was specifically up-regulated in neonatal rat cardiomyocytes. Exposure of neonatal rat cardiomyocytes to glucose and transfection with miR-373 mimic showed increased expression of miR-373 and cell size, suggesting a strong involvement of miR-373 in the glucose-induced cardiomyocyte hypertrophy. Bioinformatics analysis indicates that MEF2C is a potential target gene of miR-373. MEF2C, a transcription factor, is involved in transcriptional regulation in postnatal hearts.
      • Kolodziejczyk S.M.
      • Wang L.
      • Balazsi K.
      • DeRepentigny Y.
      • Kothary R.
      • Megeney L.A.
      MEF2 is upregulated during cardiac hypertrophy and is required for normal post-natal growth of the myocardium.
      Previous studies have demonstrated that glucose-induced cardiomyocyte hypertrophy is associated with increased activity of MEF2.
      • Feng B.
      • Chen S.
      • Chiu J.
      • George B.
      • Chakrabarti S.
      Regulation of cardiomyocyte hypertrophy in diabetes at the transcriptional level.
      To confirm this, the protein level of MEF2C was determined by Western blot. The results showed that upregulation of miR-373 decreased MEF2C expression in cultured cardiac myocytes, which suggested that MEF2C was a target gene of miR-373. To further determine whether MAPK signaling pathway is a downstream target of miR-373 that is involved in a mediated effect on cardiac myocytes, we tested whether up-regulation of miR-373 is able to influence p38 MAPK, ERK1/2, and JNK levels. As a result, transfection with miR-373 mimic did not alter levels of p38 MAPK, ERK1/2, or JNK. It appears that MAPK are not the downstream targets of miR-373. Previous study showed that p38 can directly regulate MEF2 transcription factors in hypertrophied heart.
      • Han J.
      • Molkentin J.D.
      Regulation of MEF2 by p38 MAPK and its implication in cardiomyocyte biology.
      • Akazawa H.
      • Komuro I.
      Roles of cardiac transcription factors in cardiac hypertrophy.
      These findings encouraged us to further explore the relationship between MAPK pathway and miR-373 in glucose-induced cardiomyocyte hypertrophy. To this end, we examine whether blockade of p38 MAPK, ERK1/2, or ERK with specific inhibitors is sufficient to increase miR-373 levels. Our data demonstrate that inhibition of p38 MAPK potently decreased cell size and reduced miR-373 expression. However, the ERK1/2 and JNK inhibitors had no effect on miR-373 levels. It was indicated that p38 MAPK regulates miR-373 expression but not ERK 1/2 and JNK. Inhibition of p38 MAPK also could lead to a reduction in levels of MEF2C. It is possible that p38 MAPK initiates high-glucose–induced cardiomyocyte hypertrophy by regulating miR-373–dependent repression of MEF2C.
      In conclusion, this study attempted to create a holistic view of diabetic heart tissue–specific miRNA expression profiles and predicted miRNA target genes using online databases. By integrating the GO analysis and KEGG pathway data obtained from function enrichment and pathway enrichment of tissue-specific miRNA targets, we obtained regulatory networks mediated by tissue-specific miRNAs. The current study also reveals that miR-373 is transcriptionally regulated by p38 MAPK. miR-373 protects against the high-glucose–induced cardiomyocyte hypertrophy by targeting the hypertrophic protein, MEF2C. Our findings suggest that miRNAs plays an important role in hyperglycemia-induced hypertrophy in cardiomyocytes through several signaling pathways, especially MAPK signaling pathways, leading to myocardial dysfunction in diabetes. These findings provide information for future investigations into the mechanisms and pathology of diabetic cardiomyopathy.

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